LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
Robust, randomized preconditioning for kernel ridge regression
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
APLICUR uses one modest sketch to adaptively update a CUR preconditioner interleaved with LSQR iterations, delivering convergence guarantees independent of sketch size for general large-scale least-squares problems.
PALM-KQR combines inexact ADMM warm-start with semismooth Newton ALM and low-rank preconditioning to solve large-scale kernel quantile regression more efficiently than prior solvers.
citing papers explorer
-
Accelerating Regularized Attention Kernel Regression for Spectrum Cartography
LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
-
Adaptive LSQR Preconditioning from One Small Sketch
APLICUR uses one modest sketch to adaptively update a CUR preconditioner interleaved with LSQR iterations, delivering convergence guarantees independent of sketch size for general large-scale least-squares problems.
-
Scalable Kernel Quantile Regression: A Preconditioned Augmented Lagrangian Method
PALM-KQR combines inexact ADMM warm-start with semismooth Newton ALM and low-rank preconditioning to solve large-scale kernel quantile regression more efficiently than prior solvers.